[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2773":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":28,"view_count":34,"doi":35,"paper":36,"created_at":52},2773,"IoT-Enabled Sensor Applications in Smart Healthcare, Smart Agriculture, Environmental Monitoring and Smart Energy & Safety Monitoring","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22795428","The Internet of Things (IoT) is becoming a fundamental technology for the development of sensorized intelligent systems in several domains of society. This paper presents a structured literature review on architectures for sensing and intelligence in the scope of four research areas: Smart Healthcare, Smart Agriculture, Environmental Monitoring, and Smart Energy and Safety Monitoring. Recent journal articles, systematic reviews, and conference papers were analysed to report sensor modalities, communication architectures, and learning algorithms, namely machine learning (ML) and deep learning (DL), that have been proposed in literature for each of the above research areas. A consolidated inventory of algorithms that have been reported in the above surveyed works, as well as a comparison of sensing technologies, learning techniques, and their corresponding applications, are also presented. Finally, open challenges that have been found to be recurring, i.e., sensor calibration and drift, energy efficiency, security and privacy, heterogeneous data, and the gap between proof-of-concept prototypes and large-scale scalable and practical implementations, as well as future research directions, are outlined. The purpose of this work is to provide a useful, structured literature review that can serve as a background and reference for researchers interested in sensorized IoT applications using algorithmic approaches.","物联网（IoT）正在成为社会发展多个领域中传感智能系统开发的基础性技术。本文针对四个研究领域——智能医疗、智能农业、环境监测以及智能能源与安全监测——中的传感与智能架构进行了结构化文献综述。通过分析近期期刊论文、系统性综述和会议论文，报告了上述各研究领域中文献所提出的传感器模态、通信架构以及学习算法，即机器学习（ML）和深度学习（DL）。本文还提供了在上述综述工作中所报道算法的综合清单，以及传感技术、学习技术及其相应应用的对比。最后，概述了反复出现的开放挑战，即传感器校准与漂移、能效、安全与隐私、异构数据，以及概念验证原型与大规模可扩展实际实现之间的差距，并指出了未来研究方向。本工作的目的是提供一份有用的、结构化的文献综述，为对使用算法方法的传感物联网应用感兴趣的研究人员提供背景和参考。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-16T00:00:00Z","论文",25,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,9,1,"系统性文献综述，覆盖智慧农业等四大领域的传感与智能架构，方法梳理与开放挑战总结扎实，对农业物联网研究有参考价值，但非农业专属突破性成果。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22795429",[29,30,31,32,33],"智慧农业","农业人工智能","农业物联网","传感器","环境监测",0,"10.5281\u002Fzenodo.22795428",{"doi":35,"openalex_id":37,"authors":38,"venue":10,"cited_by_count":34,"oa_url":6,"card":45,"direction":49,"ingested_from":51},"W7213433550",[39,41,43],{"name":40,"orcid":9},"Mrs.C.Nithya",{"name":42,"orcid":9},"Mr.M.K.Sampath",{"name":44,"orcid":9},"Mrs. P.Raga Keerthana",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"综述物联网传感与智能在医疗、农业、环境、能源安全四大领域的架构、算法与应用。","结构化文献综述，分析传感器模态、通信架构及机器学习\u002F深度学习算法。","梳理了各领域算法清单，指出传感器校准漂移、能效、安全隐私等共性挑战。","智慧农业 \u002F 农业物联网","农业物联网中传感器校准漂移与跨域异构数据融合的轻量化算法研究尚存空白。","openalex","2026-09-17T23:30:13.984507Z"]